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Lifelong learning agents: Discovering reusable knowledge across diverse tasks

Paper Authors:

Jorge A. Mendez,

Eric Eaton

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Key Details

Lifelong learning agents face non-i.i.d. data streams, striving to leverage past knowledge when adapting to new distributions.

Compositional representations let agents reuse knowledge chunks in novel combinations, aiding transfer across diverse tasks.

Most lifelong learning work uses monolithic models, overlooking compositionality's benefits for discovering reusable knowledge.

Explicitly modular neural architectures show promise for lifelong compositional learning in supervised and RL settings.

Quantifying and encouraging the compositionality of solutions remains an open challenge.

AI generated summary

Lifelong learning agents: Discovering reusable knowledge across diverse tasks

This paper surveys existing techniques for lifelong learning, which enable artificial agents to accumulate knowledge over time and reuse it for solving new problems. It focuses on compositional approaches, which decompose problems into reusable chunks of knowledge that combine in novel ways, facilitating transfer to diverse tasks. The survey categorizes methods along six key dimensions and highlights open challenges toward developing agents that learn, remember, and creatively reuse knowledge throughout their lifetimes.

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